Created
August 4, 2020 22:12
-
-
Save ilia-cher/2baffdd98951ee2a5f2da56a04fe15d0 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/usr/bin/env python3 | |
import torch | |
import torch.nn as nn | |
import torch.cuda.profiler as profiler | |
N = 32 | |
I = 128 | |
H = 256 | |
O = 1024 | |
# 2 Layer MLP | |
model = torch.nn.Sequential( | |
torch.nn.Linear(I, H), | |
torch.nn.Linear(H, O) | |
).cuda().half() | |
# Input and Label | |
x = torch.randn(N, I).cuda().half() | |
target = torch.empty(N, dtype=torch.long).random_(O).cuda() | |
# Loss and optimizer | |
criterion = nn.CrossEntropyLoss().cuda() | |
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) | |
optimizer.zero_grad() | |
with torch.autograd.profiler.emit_nvtx(): | |
profiler.start() | |
output = model(x) | |
loss = criterion(output, target) | |
loss.backward() | |
optimizer.step() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment